Flood Risk in Szeged before River Engineering Works: A Historical Reconstruction

Szeged situated at the confluence of the Tisza and the Maros Rivers has been exposed to significant flood risk for centuries due to its low elevation and its location on the low floodplain level. After the Ottoman (Turkish) occupation of Hungary (ended in 1686), secondary sources often reported that the town was affected by devastating floods which entered the area from north, and a great part of the town or its whole area was inundated. Natural and artificial infill reduced the flood risk to some extent after the town had been founded, but in the 19th century flood risk was mitigated by river engineering and the reconstruction of the town. The town relief was raised by a huge amount of sediment, which makes it difficult to determine the elevation of the original relief as well as the exact flood risk of the study area. However, some engineering surveys originating from the 19th century contain hundreds of levelling data in a dense control point network making possible to model the relief of the whole town preceding its reconstruction and ground infill. Based on these data, we prepared a relief model which was compared with the known data of the 1772 flood peak, from which we deduced that 60% of the town must have been inundated before it was filled up. As there could have been 50-100 cm thick natural or artificial ground infill since the 11th century, the original natural relief can be gained by deducting these data. Based on this deduction, the extent of inundation centuries ago could reach 85%, which means almost total flooding

Miért lett Szeged az 1848-49 szabadságharc egyik utolsó esélye? : a szegedi sáncok

In the last months of the War of Independence of 1848-49 the Hungarian government appointed Szeged as the place to lead the war of independence from following its retreat from the capital, the aim being to stop Haynau with the remnants of the joint military forces concentrated here. Although earlier research has made little mention of the reason why Szeged was selected for this role, by now it has become evident that the fortress complex planned around the town must have been the decisive factor. So far we have had hardly any knowledge of the fortification, its exact location, size or structure. We have recently come across two handwritten maps, which have helped us to reconstruct the whole fortification complex with high precision. To our great surprise significant parts of the fortification can easily be identified around the town. This discovery can lead to very important new findings in the research into the events of the war of independence and through the Szeged fortification it also illustrates the considerations 19th century engineers had in mind when planning a construction like this. Az 1848-1849-ben megépített szegedi sánc tehát összesen 30 önálló építményből állt, amihez még a Kamaratöltés utólagos erődítése kapcsolódott 31. elemként. A rendszer Szeged városát teljesen körülveszi, egyaránt védte a Tisza mindkét partján. Az összes sáncelem közül egyetlen egy volt („k"), aminek helyét nem sikerült azonosítani, hat esetben pedig csak valószínűsíteni tudtuk a helyüket. Az összes többi objektum azonosításával sikerrel jártunk, ami az eddigi ismereteinkhez képest új adatokkal gazdagíthatja a szabadságharc kutatását. Sikerrel igazoltuk azt is, hogy a szegedi sánc tervezésnek és megvalósításának az a több ezer éves hadművészeti alapelv volt a fő motívuma, hogy a harcászat során a természeti környezet előnyeinek hasznosítása és hátrányainak kiküszöbölése az egyik legfontosabb cél. A kiépített védelmi rendszer — eddig még nem ismert — makrokörnye-zetének bemutatása, majd az egyes objektumok mikrokörnyezetének ismertetése egyenként és összességében is alátámasztják ezt a megállapításunkat

Flood risk in Szeged before river engineering works : a historical reconstruction

Szeged situated at the confluence of the Tisza and the Maros Rivers has been exposed to significant flood risk for centuries due to its low elevation and its location on the low floodplain level. After the Ottoman (Turkish) occupation of Hungary (ended in 1686), secondary sources often reported that the town was affected by devastating floods which entered the area from north, and a great part of the town or its whole area was inundated. Natural and artificial infill reduced the flood risk to some extent after the town had been founded, but in the 19th century flood risk was mitigated by river engineering and the reconstruction of the town. The town relief was raised by a huge amount of sediment, which makes it difficult to determine the elevation of the original relief as well as the exact flood risk of the study area. However, some engineering surveys originating from the 19th century contain hundreds of levelling data in a dense control point network making possible to model the relief of the whole town preceding its reconstruction and ground infill. Based on these data, we prepared a relief model which was compared with the known data of the 1772 flood peak, from which we deduced that 60% of the town must have been inundated before it was filled up. As there could have been 50-100 cm thick natural or artificial ground infill since the 11th century, the original natural relief can be gained by deducting these data. Based on this deduction, the extent of inundation centuries ago could reach 85%, which means almost total flooding

SVD-clustering, a general image-analyzing method explained and demonstrated on model and Raman micro-spectroscopic maps

An image analyzing method (SVD-clustering) is presented. Amplitude vectors of SVD factorization (V1…Vi) were introduced into the imaging of the distribution of the corresponding Ui basis-spectra. Since each Vi vector contains each point of the map, plotting them along the X, Y, Z dimensions of the map reconstructs the spatial distribution of the corresponding Ui basis-spectrum. This gives valuable information about the first, second, etc. higher-order deviations present in the map. We extended SVD with a clustering method, using the significant Vi vectors from the VT matrix as coordinates of image points in a ne-dimensional space (ne is the effective rank of the data matrix). This way every image point had a corresponding coordinate in the ne-dimensional space and formed a point set. Clustering was applied to this point set. SVD-clustering is universal; it is applicable to any measurement where data are recorded as a function of an external parameter (time, space, temperature, concentration, species, etc.). Consequently, our method is not restricted to spectral imaging, it can find application in many different 2D and 3D image analyses. Using SVD-clustering, we have shown on models the theoretical possibilities and limitations of the method, especially in the context of creating, meaning/interpreting of cluster spectra. Then for real-world samples, two examples are presented, where we were able to reveal minute alterations in the samples (changing cation ratios in minerals, differently structured cellulose domains in plant root) with spatial resolution. © 2020, The Author(s)

Az avar kori sírrablásokról három kiskundorozsmai temető kapcsán

A szerzők három, a közelmúltban feltárt avar kori temető sírrablási szokásait elemzik, és ennek kapcsán a kérdéskör szakirodalmát áttekintve olyan általános érvényű eredményekre jutottak, amelyek jól hasznosíthatók a népvándorlás kor más népei esetében is a sírrablási szokások vizsgálatakor

SVD-clustering, a general image-analyzing method explained and demonstrated on model and Raman micro-spectroscopic maps

An image analyzing method (SVD-clustering) is presented. Amplitude vectors of SVD factorization (V1…Vi) were introduced into the imaging of the distribution of the corresponding Ui basis-spectra. Since each Vi vector contains each point of the map, plotting them along the X, Y, Z dimensions of the map reconstructs the spatial distribution of the corresponding Ui basis-spectrum. This gives valuable information about the first, second, etc. higher-order deviations present in the map. We extended SVD with a clustering method, using the significant Vi vectors from the VT matrix as coordinates of image points in a ne-dimensional space (ne is the effective rank of the data matrix). This way every image point had a corresponding coordinate in the ne-dimensional space and formed a point set. Clustering was applied to this point set. SVD-clustering is universal; it is applicable to any measurement where data are recorded as a function of an external parameter (time, space, temperature, concentration, species, etc.). Consequently, our method is not restricted to spectral imaging, it can find application in many different 2D and 3D image analyses. Using SVD-clustering, we have shown on models the theoretical possibilities and limitations of the method, especially in the context of creating, meaning/interpreting of cluster spectra. Then for real-world samples, two examples are presented, where we were able to reveal minute alterations in the samples (changing cation ratios in minerals, differently structured cellulose domains in plant root) with spatial resolution. © 2020, The Author(s)